Topography of Applied AI

Keep'in it real with AI, ML, and Large Language Models: No fluff, no hype, no spam – get the inside scoop from an industry insider.

The hottest Substack posts of Topography of Applied AI

And their main takeaways
39 implied HN points β€’ 24 Sep 23
  1. Life has unexpected challenges that force self-reflection and sharing can be cathartic.
  2. Resilience involves enduring hardships, processing pain, and choosing to rise after setbacks.
  3. Even in facing multiple setbacks, the refusal to give up and the choice to stand back up is crucial.
19 implied HN points β€’ 22 Jul 23
  1. Clarity in prompts is crucial: ensure they are easily readable and extendable.
  2. Prompts should be concise and focus on the most important information.
  3. High-quality prompts are essential, utilizing advanced techniques and capable of handling exceptions.
19 implied HN points β€’ 22 Jul 23
  1. Insightful article on the rise of AI engineering
  2. Predictions about AI and the automation of work
  3. Understanding that AI is more than just software, it's about people
10 HN points β€’ 24 Jul 23
  1. AI will amplify and augment human capabilities, not replace them
  2. Historically, technological advancements have not led to human replacement, but rather to amplification and adaptation
  3. With AI integration, management may expect more from employees, potentially leading to increased pressure and expectations
0 implied HN points β€’ 31 Jul 23
  1. Plan-and-Solve Prompting breaks down complex tasks for AI models to solve step by step.
  2. PS Prompting enhances the accuracy of AI models in handling open-ended and multi-step tasks.
  3. Prompt engineering remains crucial for improving reasoning in Large Language Models despite their advancements.
Get a weekly roundup of the best Substack posts, by hacker news affinity:
0 implied HN points β€’ 26 Aug 23
  1. Humans have historically relied on ingenuity, adaptability, and reasoning to solve problems.
  2. Modern AI systems like GPT-4 can automate parts of the problem-solving process, especially in the execution phase.
  3. As AI advances, the future may see machines managing the entire problem-solving loop, leaving humans to explore new challenges and frontiers.
0 implied HN points β€’ 13 Sep 23
  1. AI will augment humans, not replace them
  2. Human collaboration with AI needs careful consideration
  3. Focus on designing AI to complement and empower human workers
0 implied HN points β€’ 22 Jul 23
  1. Future of training large language models: data 'dark web' vs. 'clear web'
  2. Next frontier of helpful AI: proactive vs. reactive
  3. Need to build tools as they don't exist yet
0 implied HN points β€’ 20 Sep 23
  1. Chatbots are just the beginning for large language models - there is more to come!
  2. GUIs revolutionized how we interacted with computers, similar transitions may happen with LLMs.
  3. Chatbots won't be the only way to work with LLMs - GUIs may become more popular, especially for non-technical users.